Metabarcoding Chironomid Pupal Exuviae Enables Scalable Biomonitoring With High Comparability to Morphotaxonomy

Conventional morphotaxonomy-based biomonitoring requires modernization to enable timely assessments in the face of an accelerating freshwater biodiversity crisis. The Chironomid Pupal Exuviae Technique (CPET) is an established biomonitoring approach with considerable potential for integration with molecular methods, as the shed pupal skins of emerging adults provide a source of DNA. We tested the performance of DNA metabarcoding for detecting species richness, recovering beta-diversity patterns and assessing biodiversity responses to environmental gradients using 15 mock communities and 12 natural chironomid pupal exuviae samples collected from Hong Kong streams. Additionally, the effects of primer choice were assessed by comparing a chironomid-specific primer set (COI-S) against a universal, highly degenerate primer set (COI-V). Our results show that both primer sets achieved high species detection rates in mock communities (86% and 84%, respectively) and consistently recovered predefined community dissimilarities. In natural community samples, however, only COI-S produced beta-diversity patterns significantly congruent with that of the morphological dataset (Procrustes r = 0.74, p = 0.003). Both metabarcoding datasets effectively captured seasonal community shifts and demonstrated greater sensitivity to environmental gradients than traditional morphological identification. Notably, the COI-S dataset explained the highest proportion of community variation (adjusted R² = 44.3%), along the environmental gradient, outperforming both the morphotaxonomy (32.5%) and the COI-V (29.0%) analyses. Here we propose Meta-CPET, an integrated framework that combines the ecological relevance of CPET with the efficiency, scalability, and resolution of DNA metabarcoding. Our findings show that bulk-sample metabarcoding enables practical, standardized and large-scale freshwater biomonitoring while maintaining strong comparability with conventional methods.